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In sponsored search, retrieving synonymous keywords is of great importancefor accurately targeted advertising. The semantic gap between queries andkeywords and the extremely high precision requirements (>= 95\%) are two majorchallenges to this task. To the best of our knowledge, the problem has not beenopenly discussed. In an industrial sponsored search system, the retrievedkeywords for frequent queries are usually done ahead of time and stored in alookup table. Considering these results as a seed dataset, we propose adata-augmentation-like framework to improve the synonymous retrievalperformance for these frequent queries. This framework comprises two steps:translation-based retrieval and discriminant-based filtering. Firstly, wedevise a Trie-based translation model to make a data increment. In this phase,a Bag-of-Core-Words trick is conducted, which increased the data increment'svolume 4.2 times while keeping the original precision. Then we use a BERT-baseddiscriminant model to filter out nonsynonymous pairs, which exceeds thetraditional feature-driven GBDT model with 11\% absolute AUC improvement. Thismethod has been successfully applied to Baidu's sponsored search system, whichhas yielded a significant improvement in revenue. In addition, a commercialChinese dataset containing 500K synonymous pairs with a precision of 95\% isreleased to the public for paraphrase study(http://ai.baidu.com/broad/subordinate?dataset=paraphrasing).

Retrieve Synonymous keywords for Frequent Queries in Sponsored Search in a Data Augmentation Way